Multi-Agent AI
An AI architecture where multiple specialized agents collaborate on complex tasks, each focusing on a specific domain like security, performance, or code quality.
Definition
Multi-agent AI systems use multiple AI models or prompts, each specialized for different aspects of a task. In code review, this means separate agents for security vulnerabilities (trained on CVE patterns), performance issues (understanding algorithmic complexity), code style (language-specific best practices), and bug detection. Agents can work in parallel and their results are aggregated. diffray uses this approach with specialized agents: security-agent, performance-agent, bug-agent, and style-agent.
Why It Matters
Single-model approaches struggle to be experts in everything. Multi-agent systems achieve better accuracy by having specialists focus on their domains. Research shows multi-agent AI achieves 15-30% better accuracy on complex tasks compared to single-agent approaches. It also enables customization — organizations can enable/disable specific agents.
Example
A PR is analyzed by 4 specialized agents in parallel: security-agent finds a potential XSS vulnerability, performance-agent identifies an O(n²) algorithm that could be O(n), bug-agent catches a null pointer risk, and style-agent notes inconsistent naming. Results are combined into a unified review.